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HowtoComputeaLargeTree(withorwithoutanalignment)
Thistalk
• Part1:Howtogetagoodalignment• Part2:Howtogetagoodtreefromagoodalignment
• Part3:Howtogetagoodtreewithoutanalignment
1kp:ThousandTranscriptomeProject
l PlantTreeofLifebasedontranscriptomesof~1200speciesl Morethan13,000genefamilies(mostnotsinglecopy)GeneTreeIncongruence
G. Ka-Shu Wong U Alberta
N. Wickett Northwestern
J. Leebens-Mack U Georgia
N. Matasci iPlant
T. Warnow, S. Mirarab, N. Nguyen UIUC UCSD UCSD
Challenge: Alignment of datasets with > 100,000 sequences
Plus many many other people…
Multiple Sequence Alignment (MSA): an important grand challenge1
S1 = -AGGCTATCACCTGACCTCCA S2 = TAG-CTATCAC--GACCGC-- S3 = TAG-CT-------GACCGC-- … Sn = -------TCAC--GACCGACA
S1 = AGGCTATCACCTGACCTCCA S2 = TAGCTATCACGACCGC S3 = TAGCTGACCGC … Sn = TCACGACCGACA
Novel techniques needed for scalability and accuracy NP-hard problems and large datasets Current methods do not provide good accuracy Few methods can analyze even moderately large datasets Many important applications besides phylogenetic estimation
1 Frontiers in Massive Data Analysis, National Academies Press, 2013
1000-taxonmodels,orderedbydifficulty(Liuetal.,2009)
Re-aligningonatree(boos5nganMSAmethod)
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Mergesubset-alignments
EsMmateMLtreeonmerged
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Decompose dataset
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Alignsubsets
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ABCD
SATéandPASTAAlgorithms
Estimate ML tree on new alignment
Tree
Obtain initial alignment and estimated ML tree
Use tree to compute new alignment
Alignment
RepeatunMlterminaMoncondiMon,and
returnthealignment/treepairwiththebestMLscore
RNASim
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• SimulatedRNASimdatasetsfrom10Kto200Ktaxa• Limitedto24hoursusing12CPUs• Notallmethodscouldrun(missingbarscouldnotfinish)
TreeError–Simulateddata
PASTARunningTimeandScalability10 PASTA: ultra-large multiple sequence alignment
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Fig. 5. Running time comparison of PASTA and SATe. (a) Running time pro-filing on one iteration for RNASim datasets with 10K and 50K sequences (the dottedregion indicates the last pairwise merge). (b) Running time for one iteration of PASTAwith 12 CPUs as a function of the number of sequences (the solid line is fitted to firsttwo points). (c) Scalability for PASTA and SATe with increased number of CPUs.
reason SATe uses so much time is that all mergers are done hierarchically usingeither Opal (for small datasets) or Muscle (on larger datasets), and both arecomputationally expensive with increased number of sequences. For example,the last pairwise merge within SATe, shown by the dotted area in Figure 5a,is entirely serial and takes up a large chunk of the total time. PASTA solvesthis problem by using transitivity for all but the initial pairwise mergers, andtherefore scales well with increased dataset size, as shown in Figure 5b (thesub-linear scaling is due to a better use of parallelism with increased number ofsequences). Finally, Figure 5c shows that PASTA is highly parallelizable, andhas a much better speed-up with increasing number of threads than SATe does.While PASTA has a much improved parallelization, it does not quite scale uplinearly, because FastTree-2 does not scale up well with increased thread count.
Divide-and-Conquer strategy: impact of guide tree. We also investigated theimpact of the use of the guide tree for computing the subset decomposition,and hence defining the Type 1 sub-alignments. We compared results obtainedusing three di↵erent decompositions: the decomposition computed by PASTAon the HMM-based starting tree, the decomposition computed by PASTA onthe true (model) tree, and a random decomposition into subsets of size 200,all on the RNASim 10k dataset. PASTA alignments and trees had roughly thesame accuracy when the guide tree was either the true tree or the HMM-basedstarting tree (Table 3). However, when based on a random decomposition, treeerror increased dramatically from 10.5% to 52.3%, and alignment scores alsodropped substantially. Thus, the guide-tree based dataset decomposition usedby PASTA provides substantial improvements over random decompositions, andthe default technique for getting the starting tree works quite well.
• OneiteraMon
• Using• 12cpus• 1nodeonLonestarTACC• Maximum24GBmemory
• ShowingwallclockrunningMme• ~1hourfor10ktaxa• ~17hoursfor200ktaxa
1kp:ThousandTranscriptomeProject
l PlantTreeofLifebasedontranscriptomesof~1200speciesl Morethan13,000genefamilies(mostnotsinglecopy)GeneTreeIncongruence
G. Ka-Shu Wong U Alberta
N. Wickett Northwestern
J. Leebens-Mack U Georgia
N. Matasci iPlant
T. Warnow, S. Mirarab, N. Nguyen UIUC UCSD UCSD
Challenge: Alignment of datasets with > 100,000 sequences
Plus many many other people…
Length
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1KPdataset:morethan100,000p450amino-acidsequences,manyfragmentary
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1KPdataset:morethan100,000p450amino-acidsequences,manyfragmentary
Allstandardmul,plesequencealignmentmethodswetestedperformedpoorlyondatasetswithfragments.
1kp:ThousandTranscriptomeProject
l PlantTreeofLifebasedontranscriptomesof~1200speciesl Morethan13,000genefamilies(mostnotsinglecopy)GeneTreeIncongruence
G. Ka-Shu Wong U Alberta
N. Wickett Northwestern
J. Leebens-Mack U Georgia
N. Matasci iPlant
T. Warnow, S. Mirarab, N. Nguyen UIUC UCSD UCSD
Challenge: Alignment of datasets with > 100,000 sequences, many of which are fragmentary
Plus many many other people…
UPPUPP=“Ultra-largemulMplesequencealignmentusingPhylogeny-awareProfiles”Nguyen,Mirarab,andWarnow.GenomeBiology,2014.Purpose:highlyaccuratelarge-scalemulMplesequencealignments,eveninthepresenceoffragmentarysequences.
UPPUPP=“Ultra-largemulMplesequencealignmentusingPhylogeny-awareProfiles”Nguyen,Mirarab,andWarnow.GenomeBiology,2014.Purpose:highlyaccuratelarge-scalemulMplesequencealignments,eveninthepresenceoffragmentarysequences.
UsesanensembleofHMMs
UPPAlgorithmicApproach
1. Selectsmallrandomsubsetoffull-lengthsequences,andbuild“backbonealignment”
2. Constructan“EnsembleofHiddenMarkovModels”onthebackbonealignment
3. AddallremainingsequencestothebackbonealignmentusingtheEnsembleofHMMs
RNASimMillionSequences:treeerror
Using 12 TACC processors: • UPP(Fast,NoDecomp)
took 2.2 days,
• UPP(Fast) took 11.9 days, and
• PASTA took 10.3 days
RNASimMillionSequences:alignmenterror
Notes: • We show alignment error
using average of SP-FN and SP-FP.
• UPP variants have better alignment scores than PASTA.
• (Not shown: Total Column Scores – PASTA more accurate than UPP)
• No other methods tested could complete on these data
• PASTA under-aligns: its alignment is 43 times wider than true alignment (~900 Gb of disk space). UPP alignments were closer in length to true alignment (0.93 to 1.38 wider).
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Figure S32: Alignment and tree error of PASTA and UPP on the fragmentary 1000M2datasets.
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1000M2modelcondiMon
UPPismorerobusttofragmentarysequencesthanPASTA
UnderhighratesofevoluMon,PASTAisbadlyimpactedbyfragmentarysequences(thesameistrueforothermethods).UnderlowratesofevoluMon,PASTAcansMllbehighlyaccurate(datanotshown).UPPconMnuestohavegoodaccuracyevenondatasetswithmanyfragmentsunderallratesofevoluMon.
Nguyen et al. Genome Biology (2015) 16:124 Page 6 of 15
Table 2 Average alignment SP-error, tree error, and TC score across most full-length datasets
Method ROSE RNASim Indelible ROSE CRW 10 AA HomFam HomFam
NT 10K 10K AA (17) (2)
Average alignment SP-error
UPP 7.8 (1) 9.5 (1) 1.7 (2) 2.9 (1) 12.5 (1) 24.2 (1) 23.3 (1) 20.8 (2)
PASTA 7.8 (1) 15.0 (2) 0.4 (1) 3.1 (1) 12.8 (1) 24.0 (1) 22.5 (1) 17.3 (1)
MAFFT 20.6 (2) 25.5 (3) 41.4 (3) 4.9 (2) 28.3 (2) 23.5 (1) 25.3 (2) 20.7 (2)
Muscle 20.6 (2) 64.7 (5) 62.4 (4) 5.5 (3) 30.7 (3) 30.2 (2) 48.1 (4) X
Clustal 49.2 (3) 35.3 (4) X 6.5 (4) 43.3 (4) 24.3 (1) 27.7 (3) 29.4 (3)
Average !FN error
UPP 1.3 (1) 0.8 (1) 0.3 (1) 1.8 (1) 7.8 (2) 3.4 (2) NA NA
PASTA 1.3 (1) 0.4 (1) <0.1 (1) 1.3 (1) 5.1 (1) 3.3 (1) NA NA
MAFFT 5.8 (2) 3.5 (2) 24.8 (3) 4.5 (3) 10.1 (3) 2.3 (1) NA NA
Muscle 8.4 (3) 7.3 (3) 32.5 (4) 3.1 (2) 5.5 (1) 12.6 (3) NA NA
Clustal 24.3 (4) 10.4 (4) X 4.2 (3) 34.1 (4) 3.5 (2) NA NA
Average TC score
UPP 37.8 (1) 0.5 (2) 11.0 (3) 2.6 (2) 1.4 (1) 11.4 (1) 47.3 (1) 40.3 (3)
PASTA 37.8 (1) 2.3 (1) 48.0 (1) 5.4 (1) 2.3 (1) 12.1 (1) 46.1 (2) 50.0 (1)
MAFFT 31.4 (2) 0.4 (2) 7.8 (4) 0.6 (3) 0.7 (2) 12.1 (1) 45.5 (2) 46.9 (2)
Muscle 9.8 (3) <0.0 (2) 18.3 (2) 2.7 (2) 0.7 (2) 10.5 (2) 27.7 (4) X
Clustal 5.7 (4) 0.2 (2) X 3.1 (2) 0.1 (2) 11.8 (1) 38.6 (3) 31.0 (4)
We report the average alignment SP-error (the average of SPFN and SPFP errors) (top), average !FN error (middle), and average TC score (bottom), for the collection offull-length datasets. All scores represent percentages and so are out of 100. Results marked with an X indicate that the method failed to terminate within the time limit(24 hours on a 12-core machine). Muscle failed to align two of the HomFam datasets; we report separate average results on the 17 HomFam datasets for all methods and thetwo HomFam datasets for all but Muscle. We did not test tree error on the HomFam datasets (therefore, the !FN error is indicated by “NA”). The tier ranking for each methodis shown parenthetically
memory error message were marked as failures. Forexperiments on the million-sequence RNASim dataset,we ran the methods on a dedicated machine with 256GBof main memory and 12 cores until an alignment wasgenerated or the method failed. We also performed a lim-ited number of experiments on TACC with UPP’s internal
checkpointing mechanism, to explore performance whentime is not limited. All methods other than Muscle hadparallel implementations and were able to take advantageof the 12 available cores.On full-length datasets (Table 2) where nearly all meth-
ods were able to complete, PASTA was nearly always in
Table 3 Average alignment SP-error and tree error across fragmentary datasets
Method ROSE NT RNASim 10K Indelible 10K CRW
(16S.3 and 16S.T)
Average alignment SP-error
UPP 8.3 (1) 11.8 (1) 2.7 (1) 16.1 (1)
PASTA 25.2 (2) 47.7 (4) 8.8 (2) 23.3 (2)
MAFFT 32.5 (3) 25.5 (2) 51.3 (3) 24.5 (3)
Muscle 35.3 (4) 82.2 (5) 77.6 (4) 70.6 (5)
Clustal 62.0 (5) 35.0 (3) X 46.7 (4)
Average !FN error
UPP 1.9 (1) 3.1 (1) 2.5 (1) 7.4 (2)
PASTA 25.2 (3) 21.9 (3) 9.0 (2) 8.2 (2)
MAFFT 18.0 (2) 6.2 (2) 35.6 (3) 2.5 (1)
Muscle 27.5 (4) 43.6 (5) 45.2 (4) 30.1 (3)
Clustal 47.8 (5) 26.3 (4) X 37.4 (4)
We report the average alignment error (top) and average !FN error (bottom) on the collection of fragmentary datasets. Clustal-Omega failed to align any of the Indelible10000M2 fragmentary datasets and thus we mark the results with an X. The tier ranking for each method is shown in parentheses
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PASTAandUPP:boostersofMSAmethods
• PASTA– CombinesiteraMonanddivide-and-conquerto“boost”apreferred
MSAmethodtolargedatasets;weshowedresultsbasedonMAFFT• UPP
– Step1:Constructsa“backbone”treeandanalignmentonasmallrandomsubsetofthesequences
– Step2:Alignsalltheremainingsequencestothebackbonealignment
– WeshowedresultswheredefaultPASTAcomputedthebackbonealignmentandtree.
Note:PASTAandUPPcanbeusedwithanyMSAmethod.
Part2:TogetalargetreefromanMSA?
Basicapproach:maximumlikelihoodLeadingMLmethodsforlargedatasets:
– RAxML(andExaML),and– FastTree-2
Figure3.ComparisonofMLmethodsonthe16S.B.ALLdataset.
LiuK,LinderCR,WarnowT(2011)RAxMLandFastTree:ComparingTwoMethodsforLarge-ScaleMaximumLikelihoodPhylogenyEsMmaMon.PLoSONE6(11):e27731.doi:10.1371/journal.pone.0027731hhp://journals.plos.org/plosone/arMcle?id=info:doi/10.1371/journal.pone.0027731
Figure1.MissingbranchratesofMLmethodsonthesimulated1000-taxondatasets.
LiuK,LinderCR,WarnowT(2011)RAxMLandFastTree:ComparingTwoMethodsforLarge-ScaleMaximumLikelihoodPhylogenyEsMmaMon.PLoSONE6(11):e27731.doi:10.1371/journal.pone.0027731hhp://journals.plos.org/plosone/arMcle?id=info:doi/10.1371/journal.pone.0027731
FastTreevs.RAxML
• FastTree-2(MorganPriceetal.):veryfast,justaboutasaccuratetreetopologiesasRAxML,andcanhandledatasetsupto1,000,000sequences.
• RAxML(Stamatakisetal.):notnearlyfastenoughonlargenumbersoftaxa,butcandomulM-locusanalyseswell.
FastTreevs.RAxML
MainadvantageofRAxMLoverFastTreeisreallyonlyonextremelyaccuratealignments,andeventhereit’snotprobablyaboutthetreetopologybutsomeotherparameter.
Part3:TogetalargetreewithoutanMSA?
• Alignment-freeesMmaMon?Noevidence(yet)thatitisasaccurateasgoodtwo-phasemethods.
• Butalmostalignment-freeesMmaMonseemsfeasible!
DACTAL
• Divide-And-ConquerTrees(Almost)withoutalignments
• Nelesenetal.,ISMB2012andBioinformaMcs2012
• Input:unalignedsequences• Output:Tree(butnoalignment)
DACTAL
Supertree method: SuperFine
RAxML(MAFFT)
pRecDCM3
BLAST-based
Overlapping subsets
A tree for each subset
Unaligned Sequences
A tree for the entire dataset
Analysisofthe16S.Tdataset(7350RNAsequences)
Default:startwithtwo-phasetree,decomposeinto200-taxonsubsets,RunFastTree(MAFFT)onsubsets,combineusingSuperFine+MRP
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Summary:TogetalargeMSA
• Fewerthan200sequences?MAFFTlikelybestforslowlyevolvingloci,otherwisePASTAorUPP.
• Morethan200sequences?PASTAorUPP.• Fragments?UseUPP.
Summary:TogetalargetreefromanMSA?
• Maximumlikelihoodgoodapproach(highaccuracyfortreetopologypointesMmate).
• Forunder100sequences,RAxMLisgoodenough.
• Forlargerdatasets,tryFastTree-2.
OpenProblemsinMSA/treeesMmaMon
• StaMsMcalco-esMmaMonofalignmentsandtrees
• PhylogenyesMmaMongivenindels• Exploringalignmentuncertainty• EsMmaMngbeheralignmentsbycombiningesMmatedalignments
Re-aligningonatree(boos5nganMSAmethod)
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EsMmateMLtreeonmerged
alignment
Decompose dataset
A B
C D
Alignsubsets
A B
C D
ABCD
Acknowledgments
Papersavailableathhp://tandy.cs.illinois.edu/papers.htmlPASTAandUPPathhps://github.com/smirarabFunding:NSFABI-1458652andIII:AF:1513629,aFounderProfessorshipfromtheGraingerFoundaMon,andHHMI(toS.M.)Computa5onalsupport:TACC(PASTAandUPP)
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